1. Field of the Invention
The present invention relates to a resolution conversion apparatus, method, and program, which convert an image into an image of another sampling rate.
2. Description of the Related Art
Televisions and displays with high resolutions are now commonly available. Upon displaying an image, a television or display converts the number of pixels of image data into that of a panel. Especially, in upscaling, which increases the number of pixels, as a method of obtaining a sharper image than linear interpolation, a method of reconstructing a high-resolution image using information of a plurality of frames in consideration of an inverse problem of an image sensing process (deterioration process) (to be referred to as a reconstruction method hereinafter) is known.
More specifically, for example, a block defined by a square of several pixels (for example, a block of 5 pixels (horizontal)×5 pixels (vertical)) is extracted from a low-resolution image to have a certain pixel as the center, and a conversion target frame is searched for a part which has the same size as this block and includes pixels having pixel values close to the extracted block. This search is conducted to a sub-pixel precision (for example, see M. Shimizu et al., “Precise Sub-pixel Estimation on Area-based Matching,” in Proc. IEEE International Conference on Computer Vision, pp. 90-97, 2001.) After the search, the center of a found corresponding block is set as a corresponding point. As a result, a point A on a screen corresponding to another frame is associated with a point B corresponding to the conversion target frame as the same position of an object. This associating algorithm will be referred to as a block matching method hereinafter. This association is expressed by a motion vector which has the point A as a start point and the point B as an end point. Since the search is conducted to a sub-pixel precision, the start point of the motion vector corresponds to a position of a pixel, but the end point corresponds to a position where no pixel exists. Such motion vectors are calculated for all pixels of the low-resolution image, and motion vectors having each pixel on each of other low-resolution images as a start point to the conversion target frame are detected. After the motion vectors to the conversion target frame are obtained, the pixel values of the start points are allocated at the end points of the respective motion vectors as sampling values of the conversion target frame. Finally, from the non-uniformly allocated sampling points and sampling values at these points, sampling values of pixels, which are uniformly allocated in a grid pattern, of a high-resolution image are calculated. A large number of such conversion (reconstruction) methods are available, and for example, a Non-uniform interpolation method, POCS method, ML method, and MAP method are known (for example, see S. C. Park et al., “Super-Resolution Image Reconstruction: A Technical Overview,” IEEE Signal Processing Magazine, pp. 21-36, May 2003.)
The conventional method, for example, the method described in the related art, searches the conversion target frame for corresponding points with reference to respective pixels on other frames. This method suffers from the following two problems. As the first problem, since the densities of corresponding points found on the conversion target frame differ according to location, the reconstruction performance of an output image is not stable. As the second problem, since corresponding points found on the conversion target frame readily include those with low reliability, strong noise tends to be generated.